Predictive Maintenance, or PdM, programs in industrial plants are frequently implemented by assigning a technician to use portable instrumentation, such as a vibration analyzer, an ultrasonic gun, and/or an IR camera, along a predetermined route to collect data related to the operation of the equipment on this route. This information, in turn, may then be used to diagnose problems or potential problems associated with the health and/or operation of the equipment.
For example, a PdM program may include a technician or operator (hereinafter “operator”) carrying a vibration analyzer device to each machine located along a defined route. Upon reaching a particular machine that is desired to be analyzed, a vibration sensor, such as an accelerometer, is physically coupled to the machine at one or more measurement locations. Frequently, the data to be acquired at each measurement location is specified as part of the route instructions. The vibration sensor and analyzer then acquire vibration data from the measurement locations, and may output this information on a display of the analyzer.
One of the difficulties involved in collecting data in industrial plants relating to vibration and process information for PdM programs is the complexity of freeing the operator's hands to maneuver between pieces of machinery, manipulate one or more sensors, climb and hold rails, look at display, and simultaneously operate a measuring instrument for normal data collection. In many cases, the operator will wear gloves to protect his hands which may also contribute to awkwardness in accurately pressing the buttons to operate the instrument. These difficulties can result in slowing down the data collection process, diminish operator comfort and data collection quality, and introduce additional safety risks.
One solution to enable “hands-free operation” would be the use of voice control and verbal feedback. Although this technology has been applied successfully in many fields, its use in this application has presented several difficulties which have prevented considering it as an option. These difficulties are encountered due to the ambient noise present in PdM environments, and the varying level and frequency of the ambient noise coming from a variety of dynamic sources. In order to limit the impact of false positive recognition events and to provide high recognition accuracy for the correct commands in this environment, special considerations must be given to the design of the user-device interface and to selective adaption and application of speech recognition engine functions. Additionally, the number and the complexity of functions required to perform the data collection tasks presents a challenging design for a speech recognition solution. Finally, one task expected of the operator is to confirm that the data collected is of good quality and to collect additional data in the case that it is suspected that the machine may have fault conditions present. Often this is done by observing the values of measured parameters or scrutinizing the character of graphical presentations of the measured data for unusual characteristics. This aspect of the operator's task is again a challenge to handle using voice feedback without the addition of functions to automatically interpret the data and summarize the findings in a verbal synopsis. Thus, to date no workable solution using speech recognition and feedback has been available for PdM applications.